Overview

Dataset statistics

Number of variables32
Number of observations1851
Missing cells109
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory376.9 KiB
Average record size in memory208.5 B

Variable types

CAT21
NUM11

Warnings

Lich2 is highly correlated with Lich1High correlation
Lich1 is highly correlated with Lich2High correlation
Fstf has 109 (5.9%) missing values Missing
SpatAvg is uniformly distributed Uniform
TLCar is uniformly distributed Uniform
TLHGV is uniformly distributed Uniform
df_index has unique values Unique
TempDist has 845 (45.7%) zeros Zeros
SpatDist has 1568 (84.7%) zeros Zeros
UArt1 has 63 (3.4%) zeros Zeros
AUrs1 has 1660 (89.7%) zeros Zeros
AUrs2 has 1840 (99.4%) zeros Zeros

Reproduction

Analysis started2020-11-30 17:08:49.958958
Analysis finished2020-11-30 17:09:15.961641
Duration26 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct1851
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean933.5440303
Minimum0
Maximum1866
Zeros1
Zeros (%)0.1%
Memory size14.5 KiB
2020-11-30T18:09:16.038539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile92.5
Q1462.5
median936
Q31402.5
95-th percentile1773.5
Maximum1866
Range1866
Interquartile range (IQR)940

Descriptive statistics

Standard deviation540.8130821
Coefficient of variation (CV)0.5793118103
Kurtosis-1.208745946
Mean933.5440303
Median Absolute Deviation (MAD)470
Skewness-0.003498815736
Sum1727990
Variance292478.7898
MonotocityStrictly increasing
2020-11-30T18:09:16.204715image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
186510.1%
 
122810.1%
 
123210.1%
 
123410.1%
 
123610.1%
 
123810.1%
 
124010.1%
 
124210.1%
 
124410.1%
 
124610.1%
 
Other values (1841)184199.5%
 
ValueCountFrequency (%) 
010.1%
 
110.1%
 
210.1%
 
310.1%
 
410.1%
 
ValueCountFrequency (%) 
186610.1%
 
186510.1%
 
186410.1%
 
186310.1%
 
186210.1%
 

TempMax
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
(8.999, 69.0]
473 
(69.0, 117.0]
470 
(211.5, 1341.0]
463 
(117.0, 211.5]
445 
ValueCountFrequency (%) 
(8.999, 69.0]47325.6%
 
(69.0, 117.0]47025.4%
 
(211.5, 1341.0]46325.0%
 
(117.0, 211.5]44524.0%
 
2020-11-30T18:09:16.360265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T18:09:16.466698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:16.612131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length15
Median length13
Mean length13.74068071
Min length13

TempAvg
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
(2.999, 32.0]
474 
(90.0, 1326.0]
460 
(32.0, 55.0]
460 
(55.0, 90.0]
457 
ValueCountFrequency (%) 
(2.999, 32.0]47425.6%
 
(90.0, 1326.0]46024.9%
 
(32.0, 55.0]46024.9%
 
(55.0, 90.0]45724.7%
 
2020-11-30T18:09:16.757714image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T18:09:16.843297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:16.961748image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length14
Median length13
Mean length12.75310643
Min length12

SpatMax
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
(831.999, 4506.0]
466 
(8335.0, 14367.0]
463 
(14367.0, 49765.0]
462 
(4506.0, 8335.0]
460 
ValueCountFrequency (%) 
(831.999, 4506.0]46625.2%
 
(8335.0, 14367.0]46325.0%
 
(14367.0, 49765.0]46225.0%
 
(4506.0, 8335.0]46024.9%
 
2020-11-30T18:09:17.213130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T18:09:17.301292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:17.417736image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length18
Median length17
Mean length17.0010805
Min length16

SpatAvg
Categorical

UNIFORM

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
(2002.5, 3387.0]
464 
(5264.0, 17805.0]
463 
(134.999, 2002.5]
463 
(3387.0, 5264.0]
461 
ValueCountFrequency (%) 
(2002.5, 3387.0]46425.1%
 
(5264.0, 17805.0]46325.0%
 
(134.999, 2002.5]46325.0%
 
(3387.0, 5264.0]46124.9%
 
2020-11-30T18:09:17.557639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T18:09:17.649965image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:17.811095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length17
Median length17
Mean length16.50027012
Min length16

TempDist
Real number (ℝ≥0)

ZEROS

Distinct25
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.566180443
Minimum0
Maximum24
Zeros845
Zeros (%)45.7%
Memory size14.5 KiB
2020-11-30T18:09:17.948491image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q39
95-th percentile21
Maximum24
Range24
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.88910785
Coefficient of variation (CV)1.23767239
Kurtosis0.1456204672
Mean5.566180443
Median Absolute Deviation (MAD)3
Skewness1.103172777
Sum10303
Variance47.45980697
MonotocityNot monotonic
2020-11-30T18:09:18.094716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%) 
084545.7%
 
6884.8%
 
7764.1%
 
5703.8%
 
8673.6%
 
9663.6%
 
10603.2%
 
3583.1%
 
12512.8%
 
4502.7%
 
Other values (15)42022.7%
 
ValueCountFrequency (%) 
084545.7%
 
1412.2%
 
2321.7%
 
3583.1%
 
4502.7%
 
ValueCountFrequency (%) 
24271.5%
 
23211.1%
 
22271.5%
 
21311.7%
 
20191.0%
 

SpatDist
Real number (ℝ≥0)

ZEROS

Distinct220
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.50567261
Minimum0
Maximum2000
Zeros1568
Zeros (%)84.7%
Memory size14.5 KiB
2020-11-30T18:09:18.246572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile650.5
Maximum2000
Range2000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation281.6624937
Coefficient of variation (CV)3.294079622
Kurtosis18.39684028
Mean85.50567261
Median Absolute Deviation (MAD)0
Skewness4.148671881
Sum158271
Variance79333.76037
MonotocityNot monotonic
2020-11-30T18:09:18.406570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0156884.7%
 
250150.8%
 
75080.4%
 
125060.3%
 
5030.2%
 
15130.2%
 
29030.2%
 
17030.2%
 
46820.1%
 
34120.1%
 
Other values (210)23812.9%
 
ValueCountFrequency (%) 
0156884.7%
 
210.1%
 
320.1%
 
710.1%
 
1310.1%
 
ValueCountFrequency (%) 
200020.1%
 
197510.1%
 
196010.1%
 
194910.1%
 
190610.1%
 

Coverage
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
(4.999, 28.0]
492 
(41.0, 59.0]
484 
(28.0, 41.0]
440 
(59.0, 100.0]
435 
ValueCountFrequency (%) 
(4.999, 28.0]49226.6%
 
(41.0, 59.0]48426.1%
 
(28.0, 41.0]44023.8%
 
(59.0, 100.0]43523.5%
 
2020-11-30T18:09:18.577781image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T18:09:18.750815image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:18.908384image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length13
Mean length12.50081037
Min length12

TLCar
Categorical

UNIFORM

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
(999.999, 1265.0]
464 
(1522.0, 1765.0]
463 
(1765.0, 1999.0]
462 
(1265.0, 1522.0]
462 
ValueCountFrequency (%) 
(999.999, 1265.0]46425.1%
 
(1522.0, 1765.0]46325.0%
 
(1765.0, 1999.0]46225.0%
 
(1265.0, 1522.0]46225.0%
 
2020-11-30T18:09:19.048582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T18:09:19.145525image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:19.259574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length17
Median length16
Mean length16.25067531
Min length16

TLHGV
Categorical

UNIFORM

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
(499.999, 621.0]
464 
(745.0, 871.0]
463 
(871.0, 999.0]
462 
(621.0, 745.0]
462 
ValueCountFrequency (%) 
(499.999, 621.0]46425.1%
 
(745.0, 871.0]46325.0%
 
(871.0, 999.0]46225.0%
 
(621.0, 745.0]46225.0%
 
2020-11-30T18:09:19.394856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T18:09:19.512484image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:19.628248image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length16
Median length14
Mean length14.50135062
Min length14

Strasse
Categorical

Distinct17
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
A3
559 
A9
466 
A96
155 
A7
130 
A73
129 
Other values (12)
412 
ValueCountFrequency (%) 
A355930.2%
 
A946625.2%
 
A961558.4%
 
A71307.0%
 
A731297.0%
 
A61276.9%
 
A991166.3%
 
A92663.6%
 
A94372.0%
 
A70311.7%
 
Other values (7)351.9%
 
2020-11-30T18:09:19.776485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)0.1%
2020-11-30T18:09:19.911664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length2
Mean length2.30902215
Min length2

Kat
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
3
881 
7
718 
2
216 
1
 
36
ValueCountFrequency (%) 
388147.6%
 
771838.8%
 
221611.7%
 
1361.9%
 
2020-11-30T18:09:20.042100image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T18:09:20.127500image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:20.383660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Typ
Real number (ℝ≥0)

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.048082118
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Memory size14.5 KiB
2020-11-30T18:09:20.537035image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median6
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.951675744
Coefficient of variation (CV)0.3866172734
Kurtosis0.1886709966
Mean5.048082118
Median Absolute Deviation (MAD)0
Skewness-1.379065019
Sum9344
Variance3.809038212
MonotocityNot monotonic
2020-11-30T18:09:20.643028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
6129970.2%
 
130016.2%
 
31206.5%
 
71176.3%
 
5110.6%
 
440.2%
 
ValueCountFrequency (%) 
130016.2%
 
31206.5%
 
440.2%
 
5110.6%
 
6129970.2%
 
ValueCountFrequency (%) 
71176.3%
 
6129970.2%
 
5110.6%
 
440.2%
 
31206.5%
 

Betei
Real number (ℝ≥0)

Distinct9
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.276607239
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Memory size14.5 KiB
2020-11-30T18:09:20.764801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum18
Range17
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9626475579
Coefficient of variation (CV)0.422843054
Kurtosis42.7901568
Mean2.276607239
Median Absolute Deviation (MAD)0
Skewness3.802231857
Sum4214
Variance0.9266903208
MonotocityNot monotonic
2020-11-30T18:09:20.874216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
2113361.2%
 
335519.2%
 
121811.8%
 
41035.6%
 
5251.4%
 
760.3%
 
660.3%
 
840.2%
 
1810.1%
 
ValueCountFrequency (%) 
121811.8%
 
2113361.2%
 
335519.2%
 
41035.6%
 
5251.4%
 
ValueCountFrequency (%) 
1810.1%
 
840.2%
 
760.3%
 
660.3%
 
5251.4%
 

UArt1
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.388438682
Minimum0
Maximum9
Zeros63
Zeros (%)3.4%
Memory size14.5 KiB
2020-11-30T18:09:21.004991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.441722098
Coefficient of variation (CV)0.7206038909
Kurtosis0.3305547855
Mean3.388438682
Median Absolute Deviation (MAD)1
Skewness1.276964923
Sum6272
Variance5.962006804
MonotocityNot monotonic
2020-11-30T18:09:21.112994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
283144.9%
 
344724.1%
 
81658.9%
 
91246.7%
 
5904.9%
 
1864.6%
 
0633.4%
 
7351.9%
 
660.3%
 
440.2%
 
ValueCountFrequency (%) 
0633.4%
 
1864.6%
 
283144.9%
 
344724.1%
 
440.2%
 
ValueCountFrequency (%) 
91246.7%
 
81658.9%
 
7351.9%
 
660.3%
 
5904.9%
 

UArt2
Real number (ℝ)

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.188006483
Minimum-1
Maximum9
Zeros4
Zeros (%)0.2%
Memory size14.5 KiB
2020-11-30T18:09:21.228393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile9
Maximum9
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.063346849
Coefficient of variation (CV)16.29383626
Kurtosis3.611958471
Mean0.188006483
Median Absolute Deviation (MAD)0
Skewness2.328552188
Sum348
Variance9.384093916
MonotocityNot monotonic
2020-11-30T18:09:21.351460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1158485.6%
 
91337.2%
 
8693.7%
 
3382.1%
 
2110.6%
 
150.3%
 
740.2%
 
040.2%
 
520.1%
 
410.1%
 
ValueCountFrequency (%) 
-1158485.6%
 
040.2%
 
150.3%
 
2110.6%
 
3382.1%
 
ValueCountFrequency (%) 
91337.2%
 
8693.7%
 
740.2%
 
520.1%
 
410.1%
 

AUrs1
Real number (ℝ≥0)

ZEROS

Distinct15
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.866558617
Minimum0
Maximum89
Zeros1660
Zeros (%)89.7%
Memory size14.5 KiB
2020-11-30T18:09:21.490128image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile73
Maximum89
Range89
Interquartile range (IQR)0

Descriptive statistics

Standard deviation23.27724837
Coefficient of variation (CV)2.959012893
Kurtosis5.09890963
Mean7.866558617
Median Absolute Deviation (MAD)0
Skewness2.646851476
Sum14561
Variance541.8302919
MonotocityNot monotonic
2020-11-30T18:09:21.608530image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%) 
0166089.7%
 
73955.1%
 
72412.2%
 
89181.0%
 
82130.7%
 
8880.4%
 
8140.2%
 
8630.2%
 
8320.1%
 
7520.1%
 
Other values (5)50.3%
 
ValueCountFrequency (%) 
0166089.7%
 
72412.2%
 
73955.1%
 
7520.1%
 
7610.1%
 
ValueCountFrequency (%) 
89181.0%
 
8880.4%
 
8710.1%
 
8630.2%
 
8410.1%
 

AUrs2
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4667747164
Minimum0
Maximum89
Zeros1840
Zeros (%)99.4%
Memory size14.5 KiB
2020-11-30T18:09:21.721676image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum89
Range89
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.050807703
Coefficient of variation (CV)12.96301511
Kurtosis166.5196743
Mean0.4667747164
Median Absolute Deviation (MAD)0
Skewness12.94672183
Sum864
Variance36.61227386
MonotocityNot monotonic
2020-11-30T18:09:21.821165image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
0184099.4%
 
7330.2%
 
8120.1%
 
8020.1%
 
7520.1%
 
8910.1%
 
8410.1%
 
ValueCountFrequency (%) 
0184099.4%
 
7330.2%
 
7520.1%
 
8020.1%
 
8120.1%
 
ValueCountFrequency (%) 
8910.1%
 
8410.1%
 
8120.1%
 
8020.1%
 
7520.1%
 

AufHi
Real number (ℝ)

Distinct9
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02106969206
Minimum-1
Maximum9
Zeros2
Zeros (%)0.1%
Memory size14.5 KiB
2020-11-30T18:09:21.920256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile3
Maximum9
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.843055378
Coefficient of variation (CV)87.4742437
Kurtosis0.636765015
Mean0.02106969206
Median Absolute Deviation (MAD)0
Skewness1.407118823
Sum39
Variance3.396853125
MonotocityNot monotonic
2020-11-30T18:09:22.026358image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
-1140275.7%
 
338020.5%
 
4442.4%
 
5160.9%
 
830.2%
 
920.1%
 
020.1%
 
210.1%
 
110.1%
 
ValueCountFrequency (%) 
-1140275.7%
 
020.1%
 
110.1%
 
210.1%
 
338020.5%
 
ValueCountFrequency (%) 
920.1%
 
830.2%
 
5160.9%
 
4442.4%
 
338020.5%
 

Alkoh
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
-1
1826 
1
 
25
ValueCountFrequency (%) 
-1182698.6%
 
1251.4%
 
2020-11-30T18:09:22.142616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T18:09:22.222017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:22.305562image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.986493787
Min length1

Char1
Real number (ℝ)

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.5164775797
Minimum-1
Maximum6
Zeros0
Zeros (%)0.0%
Memory size14.5 KiB
2020-11-30T18:09:22.432412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile4.5
Maximum6
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.61349837
Coefficient of variation (CV)-3.124043393
Kurtosis8.099901466
Mean-0.5164775797
Median Absolute Deviation (MAD)0
Skewness3.130320983
Sum-956
Variance2.603376991
MonotocityNot monotonic
2020-11-30T18:09:22.557322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
-1169491.5%
 
5603.2%
 
4563.0%
 
6331.8%
 
280.4%
 
ValueCountFrequency (%) 
-1169491.5%
 
280.4%
 
4563.0%
 
5603.2%
 
6331.8%
 
ValueCountFrequency (%) 
6331.8%
 
5603.2%
 
4563.0%
 
280.4%
 
-1169491.5%
 

Char2
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
-1
1809 
6
 
42
ValueCountFrequency (%) 
-1180997.7%
 
6422.3%
 
2020-11-30T18:09:22.689146image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T18:09:22.773453image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:22.859065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.977309562
Min length1

Bes1
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
-1
1488 
6
357 
1
 
6
ValueCountFrequency (%) 
-1148880.4%
 
635719.3%
 
160.3%
 
2020-11-30T18:09:22.984515image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T18:09:23.073073image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:23.180052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.803889789
Min length1

Bes2
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
-1
1850 
6
 
1
ValueCountFrequency (%) 
-1185099.9%
 
610.1%
 
2020-11-30T18:09:23.441624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.1%
2020-11-30T18:09:23.536329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:23.625967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.999459751
Min length1

Lich1
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
0
1489 
2
262 
1
 
97
-1
 
3
ValueCountFrequency (%) 
0148980.4%
 
226214.2%
 
1975.2%
 
-130.2%
 
2020-11-30T18:09:23.751243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T18:09:23.834061image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:23.936560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length1
Mean length1.001620746
Min length1

Lich2
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
-1
1492 
4
343 
3
 
16
ValueCountFrequency (%) 
-1149280.6%
 
434318.5%
 
3160.9%
 
2020-11-30T18:09:24.085806image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T18:09:24.167505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:24.256286image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.806050783
Min length1

Zust1
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
0
1393 
1
413 
2
 
40
-1
 
5
ValueCountFrequency (%) 
0139375.3%
 
141322.3%
 
2402.2%
 
-150.3%
 
2020-11-30T18:09:24.411871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T18:09:24.577501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:24.771470image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length1
Mean length1.002701243
Min length1

Zust2
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
-1
1834 
2
 
17
ValueCountFrequency (%) 
-1183499.1%
 
2170.9%
 
2020-11-30T18:09:24.952644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T18:09:25.042750image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:25.135847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.990815775
Min length1

Fstf
Categorical

MISSING

Distinct7
Distinct (%)0.4%
Missing109
Missing (%)5.9%
Memory size14.5 KiB
2
804 
1
580 
3
290 
4
 
38
S
 
22
Other values (2)
 
8
ValueCountFrequency (%) 
280443.4%
 
158031.3%
 
329015.7%
 
4382.1%
 
S221.2%
 
550.3%
 
F30.2%
 
(Missing)1095.9%
 
2020-11-30T18:09:25.279648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T18:09:25.375030image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:25.521439image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length1
Mean length1.117774176
Min length1

WoTag
Categorical

Distinct8
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
Fr
330 
Do
293 
Mi
287 
Di
280 
Mo
255 
Other values (3)
406 
ValueCountFrequency (%) 
Fr33017.8%
 
Do29315.8%
 
Mi28715.5%
 
Di28015.1%
 
Mo25513.8%
 
So20411.0%
 
Sa19010.3%
 
120.6%
 
2020-11-30T18:09:25.655006image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T18:09:25.747978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:25.888838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.987034036
Min length0

FeiTag
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
-1
1804 
1
 
47
ValueCountFrequency (%) 
-1180497.5%
 
1472.5%
 
2020-11-30T18:09:26.016734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T18:09:26.094836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:26.177432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.97460832
Min length1

Month
Categorical

Distinct12
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size14.5 KiB
Jul
238 
Aug
220 
Oct
166 
Sep
162 
Jun
158 
Other values (7)
907 
ValueCountFrequency (%) 
Jul23812.9%
 
Aug22011.9%
 
Oct1669.0%
 
Sep1628.8%
 
Jun1588.5%
 
Apr1508.1%
 
Mar1427.7%
 
Nov1417.6%
 
Dec1377.4%
 
May1377.4%
 
Other values (2)20010.8%
 
2020-11-30T18:09:26.312688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T18:09:26.537358image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Interactions

2020-11-30T18:08:57.227839image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:08:57.393631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:08:57.542834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:08:57.692032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:08:57.838394image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:08:57.978750image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:08:58.112683image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:08:58.241947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:08:58.379233image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:08:58.522196image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:08:58.660485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:08:58.805154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:08:58.939169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:08:59.068242image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:08:59.202776image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:08:59.335746image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:08:59.470210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:08:59.602132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:08:59.726238image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:08:59.848367image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:08:59.970052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:00.096187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:00.230182image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:00.375291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:00.517145image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:00.664226image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:00.802249image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:01.048410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:01.181922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:01.308114image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:01.441220image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:01.578192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:01.711184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:01.851734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:01.985485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:02.112023image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:02.248637image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:02.373757image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:02.506567image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:02.628690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:02.758120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:02.886663image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:03.014261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:03.139655image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:03.271965image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:03.409229image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:03.541237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:03.693016image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:03.835871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:04.000548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:04.142576image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:04.301262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:04.448513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:04.641384image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:04.774499image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:05.035608image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:05.164942image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:05.368240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:05.540368image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:05.665895image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:05.794957image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:05.913882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:06.039358image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:06.155170image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:06.279195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:06.416134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:06.598224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:06.747184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:06.894469image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:07.042928image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:07.188556image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:07.324591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:07.447768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:07.579897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:07.709536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:07.831723image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:07.956239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:08.090207image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:08.220547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:08.346299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:08.490976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:08.636379image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:08.765426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:08.895039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:09.137006image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:09.257807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:09.374988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:09.513576image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:09.646848image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:09.778617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:09.902413image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:10.033306image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:10.153793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:10.279507image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:10.400728image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:10.525162image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:10.640092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:10.751595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:10.875564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:11.003944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:11.137704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:11.254485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:11.380519image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:11.510105image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:11.633619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:11.748311image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:11.859796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:11.976423image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:12.104184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:12.219064image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:12.346612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:12.507931image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:12.646935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:12.910345image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:13.053842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:13.198237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:13.335446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:13.459641image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:13.593900image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:13.722297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:13.846934image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-11-30T18:09:26.798545image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-11-30T18:09:27.189901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-11-30T18:09:27.510531image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-11-30T18:09:27.841933image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-11-30T18:09:28.328430image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-11-30T18:09:14.198017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:15.404194image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T18:09:15.729846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

df_indexTempMaxTempAvgSpatMaxSpatAvgTempDistSpatDistCoverageTLCarTLHGVStrasseKatTypBeteiUArt1UArt2AUrs1AUrs2AufHiAlkohChar1Char2Bes1Bes2Lich1Lich2Zust1Zust2FstfWoTagFeiTagMonth
00(211.5, 1341.0](90.0, 1326.0](4506.0, 8335.0](3387.0, 5264.0]00(59.0, 100.0](1522.0, 1765.0](621.0, 745.0]A32132-100-1-1-1-1-1-10-11-12Di1Jan
11(8.999, 69.0](2.999, 32.0](4506.0, 8335.0](3387.0, 5264.0]100(28.0, 41.0](1522.0, 1765.0](621.0, 745.0]A63632-1890-1-1-1-1-1-10-10-12Di1Jan
22(117.0, 211.5](55.0, 90.0](8335.0, 14367.0](5264.0, 17805.0]00(41.0, 59.0](1265.0, 1522.0](745.0, 871.0]A336529003-1-1-1-1-10-11-12Mi-1Jan
33(117.0, 211.5](55.0, 90.0](8335.0, 14367.0](5264.0, 17805.0]1996(41.0, 59.0](1265.0, 1522.0](745.0, 871.0]A33672-1820-1-1-1-1-1-10-11-12Mi-1Jan
44(117.0, 211.5](32.0, 55.0](14367.0, 49765.0](5264.0, 17805.0]00(4.999, 28.0](1265.0, 1522.0](499.999, 621.0]A33622-100-1-1-1-1-1-10-10-1NaNMi-1Jan
55(8.999, 69.0](2.999, 32.0](4506.0, 8335.0](2002.5, 3387.0]00(41.0, 59.0](999.999, 1265.0](745.0, 871.0]A63632-100-11-1-1-1-10-10-11Mi-1Jan
66(211.5, 1341.0](90.0, 1326.0](14367.0, 49765.0](3387.0, 5264.0]00(4.999, 28.0](999.999, 1265.0](621.0, 745.0]A97133-1720-1-1-1-1-1-10-1121Mi-1Jan
77(211.5, 1341.0](90.0, 1326.0](8335.0, 14367.0](5264.0, 17805.0]110(41.0, 59.0](1522.0, 1765.0](621.0, 745.0]A33733-100-1-1-1-1-1-1241-12Do-1Jan
88(117.0, 211.5](32.0, 55.0](4506.0, 8335.0](2002.5, 3387.0]00(41.0, 59.0](1765.0, 1999.0](871.0, 999.0]A97123-100-1-1-1-1-1-1240-11Fr-1Jan
99(69.0, 117.0](55.0, 90.0](8335.0, 14367.0](2002.5, 3387.0]0112(4.999, 28.0](1522.0, 1765.0](871.0, 999.0]A93632-100-1-1-1-1-1-1241-14Fr-1Jan

Last rows

df_indexTempMaxTempAvgSpatMaxSpatAvgTempDistSpatDistCoverageTLCarTLHGVStrasseKatTypBeteiUArt1UArt2AUrs1AUrs2AufHiAlkohChar1Char2Bes1Bes2Lich1Lich2Zust1Zust2FstfWoTagFeiTagMonth
18411857(69.0, 117.0](55.0, 90.0](831.999, 4506.0](2002.5, 3387.0]80(59.0, 100.0](1265.0, 1522.0](499.999, 621.0]A73119-1003-16-1-1-1240-12Fr-1Dec
18421858(211.5, 1341.0](32.0, 55.0](14367.0, 49765.0](5264.0, 17805.0]00(4.999, 28.0](1265.0, 1522.0](745.0, 871.0]A93632-100-1-1-1-1-1-10-10-1FFr-1Dec
18431859(211.5, 1341.0](90.0, 1326.0](14367.0, 49765.0](5264.0, 17805.0]00(4.999, 28.0](1765.0, 1999.0](621.0, 745.0]A731429003-1-1-1-1-10-10-12-1Dec
18441860(117.0, 211.5](90.0, 1326.0](14367.0, 49765.0](3387.0, 5264.0]160(28.0, 41.0](1765.0, 1999.0](499.999, 621.0]A73622-100-1-1-1-1-1-10-10-11Sa-1Dec
18451861(8.999, 69.0](2.999, 32.0](831.999, 4506.0](2002.5, 3387.0]220(59.0, 100.0](999.999, 1265.0](871.0, 999.0]A93622-100-1-1-1-1-1-10-10-11So-1Dec
18461862(8.999, 69.0](2.999, 32.0](8335.0, 14367.0](5264.0, 17805.0]110(59.0, 100.0](1765.0, 1999.0](499.999, 621.0]A93734-100-1-1-1-1-1-1240-13So-1Dec
18471863(117.0, 211.5](32.0, 55.0](4506.0, 8335.0](2002.5, 3387.0]12750(41.0, 59.0](1765.0, 1999.0](499.999, 621.0]A97622-100-1-1-1-16-10-10-11So-1Dec
18481864(69.0, 117.0](55.0, 90.0](831.999, 4506.0](3387.0, 5264.0]00(59.0, 100.0](1265.0, 1522.0](621.0, 745.0]A923622-100-11-1-1-1-1240-12So-1Dec
18491865(69.0, 117.0](55.0, 90.0](831.999, 4506.0](2002.5, 3387.0]40(59.0, 100.0](1765.0, 1999.0](871.0, 999.0]A33632-100-1-1-1-1-1-1230-12-1Dec
18501866(8.999, 69.0](55.0, 90.0](831.999, 4506.0](134.999, 2002.5]60(59.0, 100.0](999.999, 1265.0](499.999, 621.0]A712622-100-1-1-1-1-1-10-10-11Di-1Dec